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Updated: Sep 15, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Topological velocity inference from spatial transcriptomic data.

Yichen Gu1, Jialin Liu2, Kun H Lee2

  • 1Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA.

Nature Biotechnology
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Topological Velocity Inference (TopoVelo), a novel model to analyze cell fate transitions in tissues using spatial transcriptomic data. TopoVelo reveals how cell interactions and spatial dynamics drive tissue development and differentiation.

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Genomics

Background:

  • Understanding tissue development requires integrating spatial and temporal dynamics of cell fate transitions.
  • Cell-cell interactions, niche factors, and migration are crucial drivers of tissue morphogenesis.
  • Current models often lack the spatial resolution to capture these complex dynamics.

Purpose of the Study:

  • To develop a computational framework, Topological Velocity Inference (TopoVelo), for jointly inferring spatial and temporal cell fate dynamics.
  • To apply TopoVelo to spatial transcriptomic data for analyzing tissue development and cell differentiation.
  • To provide a foundation for modeling collective cell behaviors in developing tissues.

Main Methods:

  • Extension of the RNA velocity framework using spatially coupled differential equations to model single-cell gene expression dynamics.
  • Estimation of cell velocity from spatial transcriptomic data, specifically from developing mouse cerebral cortex.
  • Generation and analysis of Slide-seq data from in vitro models of human development.

Main Results:

  • TopoVelo successfully infers spatial and temporal cell fate dynamics from spatial transcriptomic data.
  • The model identifies interpretable spatial cell state dependencies linked to ligand-receptor gene expression.
  • Revealed spatial signatures of mouse neural tube closure and spatial patterns of early human differentiation.

Conclusions:

  • TopoVelo offers a novel approach to incorporate spatial and temporal dimensions into cell fate transition modeling.
  • The framework advances the understanding of how local cellular interactions and tissue-wide dynamics contribute to development.
  • This work lays the groundwork for future studies on the collective dynamics of cells within complex tissues.